RESUMEN
Anthropogenic climate change is increasingly threatening interpersonal violence, yet global evidence for related impacts and potential transmission mechanisms remains limited. We examine whether and how climate change, particularly climate extremes, affects interpersonal violence. Using the panel data of 140 countries and regions from 2000 to 2019, we find that hot and wet extremes precipitated increase in homicide rates globally. Economic level, inequality, and resources scarcity were important intermediaries through which climate extremes affected homicide, while the direct effects still dominated the total effects. We then reveal the heterogeneous effects of climate extremes, further suggesting that poor countries and regions with relatively small contributions to climate change were particularly sensitive to climate extremes. These findings elucidate a strong climate-violence link, helping explain implications of facilitating violence prevention and mitigating climate change.
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Homicidio , Violencia , Cambio ClimáticoRESUMEN
In this article, the maximum and minimum daily temperature data for Indian cities were tested, together with the predicted diurnal temperature range (DTR) for monthly time horizons. RClimDex, a user interface for extreme computing indices, was used to advance the estimation because it allowed for statistical analysis and comparison of climatological elements such time series, means, extremes, and trends. During these 69 years, a more erratic DTR trend was seen in the research area. This study investigates the suitability of three deep neural networks for one-step-ahead DTR time series (DTRTS) forecasting, including recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and auto-regressive integrated moving average exogenous (ARIMAX). To evaluate the effectiveness of models in the testing set, six statistical error indicators, including root mean square error (RMSE), mean absolute error (MAE), coefficient of correlation (R), percent bias (PBIAS), modified index of agreement (md), and relative index of agreement (rd), were chosen. The Wilson score approach was used to do a quantitative uncertainty analysis on the prediction error to forecast the outcome DTR. The findings show that the LSTM outperforms the other models in terms of its capacity to forget, remember, and update information. It is more accurate on datasets with longer sequences and displays noticeably more volatility throughout its gradient descent. The results of a sensitivity analysis on the LSTM model, which used RMSE values as an output and took into account different look-back periods, showed that the amount of history used to fit a time series forecast model had a direct impact on the model's performance. As a result, this model can be applied as a fresh, trustworthy deep learning method for DTRTS forecasting.
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Aprendizaje Profundo , Temperatura , Ciudades , Monitoreo del Ambiente , Predicción , IncertidumbreRESUMEN
The analysis of multi-temporal and spatial trends of rainfall in a river basin is an essential approach for water resource planning and management approach. In this study, a combination of trend analysis and spatial-temporal variability of the rainfall for 1970-2017 was applied to examine rainfall distribution patterns in a coastal watershed, Santa Maria da Vitória River Basin (southeastern Brazil). Data from 42 meteorological stations were analyzed using kriging as a geostatistical tool for point data interpolation. Trends in rainfall were computed using the RClimDex package with eleven extreme climate indices. The results have shown spatial and temporal rainfall variability, with drought events becoming more persistent in recent years in the upper sector of the basin, where agricultural land use prevails. Water shortage may impact crops and threatening the water supply and hydropower production. Trend analysis suggests that the annual total wet-day precipitation (PRCPTOT) increases in the coastal section and decreases in the upper basin sector. Consecutive dry days (CDD) and consecutive wet days (CWD) show a strong positive tendency in the lower basin section, where the metropolitan area is located, flooding risks increase in response to positive trends of intensive short-term rainfall events. These results support managers developing and planning sustainability strategies to assure water security and subsidize adaptative responses to extreme hydrological events.